The logistic function, also known as the sigmoid function, is computed as
\(\frac{1}{1+exp(-\textbf{x})}\).

Commonly, the sigmoid is used to squash the real-valued output of a linear model
\(wTx+b\) into the [0,1] range so that it can be interpreted as a probability.
It is suitable for binary classification or probability prediction tasks.

Note

Use the LogisticRegressionOutput as the final output layer of a net.

The storage type of label can be default or csr

LogisticRegressionOutput(default, default) = default

LogisticRegressionOutput(default, csr) = default

The loss function used is the Binary Cross Entropy Loss:

\(-{(y\log(p) + (1 - y)\log(1 - p))}\)

Where y is the ground truth probability of positive outcome for a given example, and p the probability predicted by the model. By default, gradients of this loss function are scaled by factor 1/m, where m is the number of regression outputs of a training example.
The parameter grad_scale can be used to change this scale to grad_scale/m.